Popularity bias has been largely investigated from the item-centered perspective, that is, how frequently popular items appear in recommendation lists. The item-centered perspective study ignores users' interest in popular or less popular items, which causes a limitation as the popularity bias does not affect users equally [1]}. Recently, Abdollahpouri et al. [2]} have conducted a research study to look at the popularity bias from a different perspective: the users'. To shed light on this topic, suppose a user rated 40 long-tail (less-popular) items and 60 popular items. Therefore, we expect the recommendation algorithm to end up with the same ratio of popular items in the recommended list presented to this user. Despite our expectations, most recommendation algorithms generate a recommendation list including close to 100 popular items. Furthermore, Abdollahpouri et al. [2]} evaluate how popularity bias leads to unfair treatment of different user groups according to their interests in popular movies. Their experiments demonstrate that the state-of-the-art recommendation algorithms can not comprehensively understand users who tend to be interested in unpopular items [4]}.
